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Implementation depth

Guides

The long-form reference tier: how to implement what the audit measures, with our own production setup as the worked example. Fewer pieces, more depth.

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AI crawler directory: who fetches your site, and what to allow

The working list of AI crawlers worth a robots.txt decision: who operates each one, what it feeds (training, search index, or live answers), and a decision framework, with this site's 15-crawler allowlist as the example.

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How to write an llms.txt that actually gets read

A working llms.txt is a curated index, not a sitemap dump. This guide walks through the three-file setup running on this site (llms.txt, llms-full.txt, and a handshake briefing), with the real files as the worked example.

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JSON-LD recipes for AI visibility: the five blocks that matter

Structured data is the weakest layer in the AI-readiness benchmark. Five copy-pasteable JSON-LD recipes fix most of it: Organization, Person, WebSite, FAQPage, and Dataset, annotated from this site's production graph.

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The AI visibility checklist: 33 checks, in scoring order

Every binary check that decides whether AI engines can fetch, parse, trust, and cite your site, organized by the nine sections of the audit rubric and ordered by weight. Work it top to bottom.

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